OHI British Columbia | OHI Science | Citation policy
OHI British Columbia | OHI Science | Citation policy
knitr::opts_chunk$set(fig.width = 6, fig.height = 4, fig.path = 'Figs/',
echo = TRUE, message = FALSE, warning = FALSE)
library(ohicore) ### devtools::install_github('ohi-science/ohicore')
source('~/github/ohibc/src/R/common.R')
dir_ohibc <- '~/github/ohibc'
dir_calc <- file.path(dir_ohibc, 'calc_ohibc')
dir_master <- file.path(dir_calc, 'master')
source(file.path(dir_calc, 'calc_scores_fxns.R'))
### provenance tracking
# library(provRmd); prov_setup()Dark grey is status; grey dotted is score; red is pressures; green is resilience.
scores_overall <- read_csv(file.path(dir_calc, 'scores_all.csv')) %>%
filter(dimension != 'future') %>%
filter(region_id != 0) %>%
filter(!goal %in% c('Index', 'SP', 'FP', 'BD')) %>%
group_by(goal, dimension, year) %>%
summarize(mean_score = mean(score, na.rm = TRUE)) %>%
ungroup() %>%
spread(dimension, mean_score)
dim_plot <- ggplot(scores_overall,
aes(x = year, y = status)) +
ggtheme_plot() +
geom_line(color = 'grey20', alpha = .7, size = 1.5) +
geom_line(aes(y = score),
color = 'grey30', linetype = '1111',alpha = .7, size = 1) +
geom_line(aes(y = pressures), color = 'darkred', alpha = .7, size = .5) +
geom_line(aes(y = resilience), color = 'darkgreen', alpha = .7, size = .5) +
scale_x_continuous(breaks = scores_overall$year %>% unique() %>% sort) +
scale_y_continuous(limits = c(0, 100)) +
theme(axis.text.x = element_text(angle = 90),
axis.title.y = element_blank()) +
facet_wrap( ~ goal)
print(dim_plot)
scores <- read_csv(file.path(dir_calc, 'scores_all.csv')) %>%
filter(dimension %in% c('trend')) %>%
left_join(get_rgn_names(), by = c('region_id' = 'rgn_id'))
for(goalname in scores$goal %>% unique() %>% sort) {
# goalname <- scores$goal[1]
scores_tmp <- scores %>%
filter(goal == goalname)
trend_plot <- ggplot(scores_tmp %>%
filter(region_id != 0),
aes(x = year, y = score, color = rgn_name)) +
ggtheme_plot() +
geom_line(aes(group = region_id), alpha = .7) +
scale_x_continuous(breaks = scores_tmp$year %>% unique() %>% sort) +
scale_y_continuous(limits = c(-1, 1)) +
theme(axis.text.x = element_text(angle = 30)) +
labs(color = goalname) +
guides(colour = guide_legend(override.aes = list(size = 3)))
print(trend_plot)
}
prs_scores <- read_csv(file.path(dir_calc, 'scores_all.csv')) %>%
filter(dimension %in% c('pressures')) %>%
left_join(get_rgn_names(), by = c('region_id' = 'rgn_id'))
for(goalname in prs_scores$goal %>% unique() %>% sort) {
# goalname <- scores$goal[1]
scores_tmp <- prs_scores %>%
filter(goal == goalname)
prs_plot <- ggplot(scores_tmp %>%
filter(region_id != 0),
aes(x = year, y = score, color = rgn_name)) +
ggtheme_plot() +
geom_line(aes(group = region_id), alpha = .7) +
scale_x_continuous(breaks = scores_tmp$year %>% unique() %>% sort) +
# scale_y_continuous(limits = c(-1, 1)) +
theme(axis.text.x = element_text(angle = 30)) +
labs(color = goalname) +
guides(colour = guide_legend(override.aes = list(size = 3)))
print(prs_plot)
}
res_scores <- read_csv(file.path(dir_calc, 'scores_all.csv')) %>%
filter(dimension %in% c('resilience')) %>%
left_join(get_rgn_names(), by = c('region_id' = 'rgn_id'))
for(goalname in res_scores$goal %>% unique() %>% sort) {
# goalname <- scores$goal[1]
scores_tmp <- res_scores %>%
filter(goal == goalname)
res_plot <- ggplot(scores_tmp %>%
filter(region_id != 0),
aes(x = year, y = score, color = rgn_name)) +
ggtheme_plot() +
geom_line(aes(group = region_id), alpha = .7) +
scale_x_continuous(breaks = scores_tmp$year %>% unique() %>% sort) +
# scale_y_continuous(limits = c(-1, 1)) +
theme(axis.text.x = element_text(angle = 30)) +
labs(color = goalname) +
guides(colour = guide_legend(override.aes = list(size = 3)))
print(res_plot)
}
source('~/github/ohibc/src/R/plot_flower.R')
index_scores <- read_csv(file.path(dir_calc, 'scores_all.csv')) %>%
filter(region_id == 0) %>%
filter(dimension %in% c('score')) %>%
filter(goal != 'Index')
plot_wts <- read_csv(file.path(dir_calc, 'conf/goals.csv')) %>%
select(order = order_hierarchy,
goal, parent, goal_label = name_flower,
weight)
index_scores <- index_scores %>%
left_join(plot_wts, by = 'goal') %>%
arrange(order) %>%
filter(!goal %in% plot_wts$parent)
for(yr in index_scores$year %>% unique() %>% sort()) {
# yr <- 2016
scores_tmp <- index_scores %>%
filter(year == yr)
index_flower <- plot_flower(scores_tmp, show_plot = FALSE) +
labs(title = paste0('OHIBC ', yr))
print(index_flower)
}
scores_all <- read_csv(file.path(dir_calc, 'scores_all.csv')) %>%
filter(region_id == 0) %>%
spread(dimension, score) %>%
group_by(goal, region_id) %>%
mutate(pred_future_status = lag(future, 5))
score_compare_plot <- ggplot(scores_all, aes(x = pred_future_status, y = status, color = goal)) +
ggtheme_plot() +
geom_abline(slope = 1, intercept = 0, color = 'darkred') +
geom_point(size = 2)
plotly::ggplotly(score_compare_plot)scores_all <- read_csv(file.path(dir_calc, 'scores_all.csv')) %>%
filter(region_id == 0) %>%
filter(goal != 'Index') %>%
filter(dimension %in% c('status', 'future')) %>%
mutate(dimension = str_replace(dimension, 'status', 'obs_status'),
dimension = str_replace(dimension, 'future', 'pred_status'),
year = ifelse(dimension == 'pred_status', year + 5, year))
for(gl in (scores_all$goal %>% unique())) { ### gl = 'AO'
scores_goal <- scores_all %>%
filter(goal == gl)
score_compare_plot <- ggplot(scores_goal, aes(x = dimension, y = score, label = year)) +
ggtheme_plot() +
geom_point(size = 2) +
geom_line(size = 1, alpha = .5, aes(group = year), alpha = .7) +
labs(y = gl)
print(score_compare_plot)
}
Clipped to 1990 and later; some data layers go back farther but these will not typically inform scores except as reference points.
